1. Field of the Invention
The present invention generally relates to a method for utilizing adaptive computer-aided detection of pulmonary nodules in thoracic Computed Tomography images using hierarchical vector quantization and an apparatus for same.
2. Description of the Related Art
Lung cancer remains the number one killer in all cancer related deaths in the United States. Screening of lung, i.e. pulmonary, nodules is recommended as an effective prevention paradigm. A pulmonary nodule is typically a round or oval-shaped growth in the lung, generally less than three centimeters in diameter, with a larger growth typically referred to as a pulmonary mass, which is more likely to be malignant, i.e., cancerous, rather than benign, i.e., non-cancerous.
Several conventional methods exist to perform a virtual biopsy on detected lung nodules. However, conventional virtual biopsy raises challenging issues due to, inter alia, difficulty of differentiating between malignant and benign nodules. Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance for non-invasive cancer detection. Lung nodule diagnosis (CADx) based on Computed Tomography (CT) imaging has shown the potential to perform the task of differentiation. However, Computer Aided nodule Detection (CADe), of nodules from CT scans raises other challenging issues in efforts to prevent lung cancer due to the high volume of CT data that radiologists must read.
Research on the shape description and growth evaluation of nodules has generated encouraging results on tumor classification, but the results are moderate for the aspect of consideration of outside characters on the nodule surface, with shape differences research requiring more accurate segmentation of nodules, which remains a difficult topic. Another reason is waiting time for the evaluation of nodule growth, which requires an elapse of time between scans, thereby delaying potential cancer treatment. Prior research focused on shape and growth principles of pulmonary nodules to distinguish malignancy. The scant work on textural features focused on calculation of features from a gray-level co-occurrence matrix.